Greenhouse gas emissions from municipal wastewater treatment facilities in China from 2006 to 2019

Wastewater treatment plants (WWTPs) alleviate water pollution but also induce resource consumption and environmental impacts especially greenhouse gas (GHG) emissions. Mitigating GHG emissions of WWTPs can contribute to achieving carbon neutrality in China. But there is still a lack of a high-resolution and time-series GHG emission inventories of WWTPs in China. In this study, we construct a firm-level emission inventory of WWTPs for CH4, N2O and CO2 emissions from different wastewater treatment processes, energy consumption and effluent discharge for the time-period from 2006 to 2019. We aim to develop a transparent, verifiable and comparable WWTP GHG emission inventory to support GHG mitigation of WWTPs in China.


Background & Summary
Municipal wastewater treatment facilities are the main technical solution to mitigating water pollution. But wastewater purification in WWTPs and other treatment facilities always comes at the cost of energy consumption, use of chemicals and environmental impacts 1,2 , among which, GHG emissions are of most concern 3,4 . Even though GHG emissions from wastewater make only a small contribution to global anthropogenic GHG emissions, it is still important to map GHG emissions from wastewater treatment systems, and to set reasonable targets for mitigation of GHG emissions 5,6 . To achieve these purposes, a comprehensive GHG inventory is a prerequisite. There have been numerous studies establishing GHG accounts of WWTPs 7-13 , but there still exist challenges and problems.
Current GHG accounts often do not consider differences of treatment processes/technologies. The accounting of GHG emissions of WWTPs at the regional level mainly uses IPCC emission factors, where the centralized biological treatment processes are only categorized into aerobic and anaerobic processes but neglect the differentiation of sub-categories of aerobic or anaerobic technologies 7,10-14 , leading to large uncertainties of GHG emission factors. To accurately account GHG emissions in WWTPs, detailed processes/technologies should be considered and analysed.
Frequently, only CH 4 and/or N 2 O are accounted for, excluding CO 2 emissions of biological treatment processes as 'these are generally derived from modern (biogenic) organic matter in human excreta or food waste and should not be included in national total emissions (IPCC 2019, Volume 5, Chapter 6, Page 7)' 15 . But intensive research has shown that a significant amount of fossil CO 2 are directly emitted from WWTPs, and assuming that all direct CO 2 emissions are biogenic may underestimate GHG emissions [16][17][18][19][20] .
Dissolved GHG in the treated effluent themselves have the potential to be released. In addition, many waterways are in eutrophic or nutrient-rich conditions, which can further induce discharged wastewater to increase GHG emissions 15 . However, GHG emissions from receiving waters are rarely accounted for, due to a lack of data of the water quality of the recipient body of water and downstream discharge pathways. Even though some studies considered off-site emissions from the treated effluent, only one discharge pathway of entering rivers, lakes or oceans was assumed [7][8][9] . To account emissions from different discharge pathways (such as direct discharge into rivers, lakes, reservoirs, seas, soil, and sewage irrigated farmland) is essential for identifying key emission sources, GHG composition and their contribution to the whole wastewater treatment system.
Existing reginal-or national-level studies on GHG emissions accounting of wastewater treatment systems are not comparable. This is mainly due to different emission factors and data sources in different studies. For example, Zhao, et al. 10 used firm-level activity data and IPCC 2006 emission factors to calculate CH 4 emissions, while emission factors of Yan, et al. 11 were obtained from the average of four references excluding IPCC emission factors, and provincial-level activity data from China Environment Yearbook and China Statistical Yearbook. Differences in applied methodology and data sources contribute to a factor 38 difference in calculated CH 4 emissions for the same year.
To solve the above gaps, we constructed a high-resolution (firm-level) and time series (from 2006 to 2019) GHG emission inventory of WWTPs in China. Emission sources include on-site emissions from biological treatment processes and off-site emissions from energy consumption and discharge pathways of the WWTP. We distinguished between 10 potential pathways: direct and indirect (after sewers) discharge into seas; direct and indirect discharge into rivers, lakes, reservoirs etc.; municipal WWPTs; direct discharge onto sewage irrigated farmlands; discharge onto land; other facilities (decentralized wastewater treatment facilities); centralized industrial WWTPs and other discharge pathways. To account for the different emission potentials of different treatment technologies, we calculated emissions based on 48 separate biological, physical, chemical and physicochemical technologies and their combinations. GHG emission factors of different biological treatment technologies in line with China's conditions were obtained from the literature. Three GHG were estimated in this research, i.e., CO 2 , N 2 O and CH 4 . We did not distinguish between fossil CO 2 and biogenic CO 2 emissions from biological treatment but regarded CO 2 emission as the sum of fossil CO 2 and biogenic CO 2 emissions.

Methods
We include GHG emissions from domestic wastewater treated by municipal WWTPs and other facilities in this paper. The other facilities mainly collect and treat wastewater discharged from residential areas, tourist facilities, resorts, nursing homes, airports, railway stations, and other public places. Domestic wastewater collected by both municipal WWTPs, and other facilities maybe mixed with industrial wastewater under certain conditions. In this case, IPCC 2019 suggests that the mixed domestic and industrial wastewater can be considered as domestic wastewater 15 .
GHG emissions of a WWTP result from on-site and off-site emissions. On-site emissions are usually defined as emissions induced by wastewater and sludge treatment processes of WWTPs 21,22 . In our study, the system boundary excludes GHG emissions from sludge treatment and disposal processes in a WWTP due to lack of data, even though it is reported that sewage sludge treatment and disposal processes account for about 40% GHG emissions in wastewater systems 23 . On the other hand, generated CH 4 emissions from a WWTP are rarely recovered or flared in China, we regard recovered or flared CH 4 emissions as being zero. Therefore, on-site emissions only refer to emissions from wastewater treatment procedures in this research. For various wastewater treatment technologies, biological treatment technologies generate on-site GHG emissions during wastewater treatment processes, but physical, chemical, and physicochemical treatment technologies do not. Off-site emissions refer to emissions from effluent, electricity consumption, production and transportation of chemicals. But we exclude off-site emissions generated by chemicals' production and transportation due to lack of data for each WWTP, and they being negligible compared with electricity consumption 13 . CO 2 emissions from electricity consumption are fossil CO 2 , because they come from coal-fired power generation, but CO 2 emissions generated by on-site wastewater treatment and off-site effluent are mixed with fossil CO 2 and biogenic CO 2 , as influent and effluent COD may contain both fossil and biogenic carbon. Figure 1 shows a flowchart of the construction of the firm-level GHG emission inventory of wastewater treatment facilities from 2006 to 2019 in China. The first step to quantify GHG emissions of a WWTP is to judge the applied treatment technology. If the WWTP adopts a biological process, on-site emissions from the biological treatment process are calculated. Otherwise, off-site emissions from electricity consumption and the discharge pathway for each WWTP are quantified. Calculation of GHG emissions from each emission source was based on the multiplication of emission factors and activity data. The activity data for each WWTP was collected from China Environmental Statistics Database (CESD) 24-37 . Classification of wastewater treatment technologies and its priority. To examine GHG emissions of different wastewater treatment processes, we need to decide the category of technology applied in each WWTP. In most cases, a WWTP has a primary, secondary or tertiary treatment process, and for each process, especially in secondary treatment, more than one technology may be applied. It is impossible to quantify on-site GHG emissions for each technology, since we only collected data on concentration of influent and effluent pollutants for the whole WWTP, rather than for each technology or process. Therefore, to simplify the calculations of on-site GHG emissions, we first need to judge the main category of treatment technology of a WWTP, and then choose the corresponding emission factors of CH 4 , N 2 O and CO 2 to calculate GHG emissions generated by biological treatment processes. The technology classification is presented in Table 1. A decision tree for determining the category of treatment technology of a WWTP is shown in Fig. 2. On-site emissions from biological treatment. CH 4 , N 2 O and CO 2 emissions estimated by this study. WWTPs or other treatment facilities, which have biological treatment processes, emit CH 4 , N 2 O and CO 2 directly, which were calculated by Eq. 1.1, 1.2 and 1.3, respectively. The CH 4 , N 2 O and CO 2 emission factors of different biological treatment processes adopted in this study were obtained from the literature, and most were studies on GHG emission factors of existing Chinese WWTPs. On the other hand, some emission factors were adopted from the IPCC 2019 report, laboratory-based studies or other models, because of a lack of studies on emission factors of full-scale wastewater treatment processes. Detailed CH 4 , N 2 O and CO 2 emission factors from the literature were summarised in Table S1, Table S2 and Table S3, respectively. We obtained the minimum, maximum and average values of emission factors for each biological treatment process. The average values of emission factors were defined as the default emission factors in this study, and they are shown in Table 2. We also list IPCC 2019 emission factors of biological treatment processes in Table 2      www.nature.com/scientificdata www.nature.com/scientificdata/ Calculation of COD removed in the form of sludge.
where, S COD i , (g COD removed as sludge/year) is the COD removed in the form of sludge in the ith WWTP, COD removed i , (g COD/year) is the COD removed of the ith WWTP. Y obs j , (g VSS/ g COD) is the observed sludge yield of process j in the ith WWTP. 1.42 (g COD/ g VSS) is the conversion factor that determine biomass concentration in terms of COD 39 . COD in and COD out are influent and effluent COD concentration of the ith WWTP. V wastewater is the volume of treated wastewater in the ith WWTP. The coefficient of Y obs j , (g VSS/ g COD) for each www.nature.com/scientificdata www.nature.com/scientificdata/ process is from Chen et al. 40 . Since a membrane bioreactor (MBR) is the combination of an enhanced activated sludge process and a membrane process, its Y obs j , was estimated by the average value of observed sludge yield of an enhanced activated sludge process and a biofilm process. Coefficients Y obs j , of different treatment processes are shown in Table 3.
off-site emissions from discharge pathways. Treated wastewater was discharged in one of 10 different pathways. Table 5 shows emission factors of CO 2 , N 2 O and CH 4 of each discharge pathway. The effluent emission factors of CH 4 and N 2 O were adopted from IPCC 2019, while the CO 2 emission factors of the treated effluent were derived from the appendix of IPCC 2019 (IPCC 2019, Volume 5, Chapter 6, Page 59-Page 60) 15 . The detailed derivation process of CO 2 emission factor of effluent discharge refers to Supplementary Information 'CO 2 emission factor of effluent discharge' . Emissions from discharge pathways were calculated by Eq. 2.1-2.3:  Table 2. Default GHG emission factors of biological treatment technologies in this study and in IPCC 2019. Note: The CH 4 , N 2 O and CO 2 emission factors of different biological treatment processes adopted in this study were obtained from the literature. Some emission factors were from studies on GHG emission factors of Chinese WWTPs. However, because of a lack of studies on emission factors of full-scale wastewater treatment processes in China, emission factors of some specific treatment technologies were adopted from the IPCC 2019 report (CH 4 and N 2 O emission factors of anaerobic biological treatment processes (12)(13)(14)(15)(16) and stabilization pond, constructed wetland and land treatment method (17-26)), laboratory-based studies (N 2 O emission factors of biofilm processes (8-11)) or other models (CO 2 emission factors of aerobic biological treatment process (1), activated sludge process (2), biofilm processes (8)(9)(10)(11), and CO 2 and CH 4 emission factors of anaerobic biological treatment processes (12)(13)(14)(15)(16) off-site emissions from electricity consumption. The calculation of GHG emissions from electricity consumption is shown in Eq. 3.1. Baseline emission factors for regional power grids in China [41][42][43][44] were used in this study. Only CO 2 is considered for emission factors for regional power grids without considering N 2 O and CH 4 due to their small contributions. China's baseline emission factors for regional power grids are presented in Table 4.
where, CO2 ele i , is the CO 2 emission from electricity consumption (kg CO 2 /year). EF ele CO j , 2, (kg CO 2 /kWh) denotes the CO 2 emission factor of province j of the studied WWTP. Ele con i , (kWh/year) refers to the electricity consumption of the ith WWTP.
Uncertainty analysis. The uncertainty of GHG emissions was mainly caused by emission factors. Since calculation of activity data of each WWTP was based on annual on-site monitored data of the volume of treated   www.nature.com/scientificdata www.nature.com/scientificdata/ wastewater, influent and effluent concentration of pollutants and electricity consumption, there is no uncertainty for activity data. We analysed GHG emissions uncertainty induced by biological treatment processes and discharge pathways. The uncertainty caused by electricity consumption was not considered, because China's regional power grid baseline emission factors are based on specific values rather than ranges.
For the emission factors of biological treatment processes, we acquired the minimum, maximum and average emission factors of each technology from the literature. Then, we used the following Eq Since the CH 4 emission factor was determined by the multiplication of the maximum producing potential (B 0 ) and the methane correction factor (MCF), its uncertainty was measured by Eq. 4.3. The uncertainty of B 0 (U B0 ) is ± 30% in IPCC 2019, and the uncertainty of MCF (U MCF ) was determined by Eq. 4.1 and 4.2. The uncertainties of N 2 O and CO 2 emission factors of discharge pathways were calculated by Eq. 4.1 and 4.2.
We applied Monte Carlo simulations to analyse the combined uncertainty of emission factors and activity data. Emission factors of CH 4 , N 2 O and CO 2 of biological treatment processes and discharge pathways all follow triangular distributions, because 'upper and lower and a preferred value are provided (IPCC 2006, Volume 1, Chapter 3, Page 22)' 15 in this study. Random sampling on emission factors was performed 100,000 times, then multiplied by activity data of each GHG in each WWTP, generating 100,000 values for GHG emissions. Finally, uncertainty ranges of 95% confidence intervals of GHG emissions were adopted.
Other causes that may induce uncertainties include 'Measurement error' , 'Lack of completeness' and 'Misreporting or misclassification' . With regard to the measurement error in a real WWTP, the measured influent and effluent concentration of pollutants and electricity consumption may be incorrect. But this uncertainty is difficult to quantify and control in this study. In terms of lack of completeness, the original data was incomplete for all WWTPs. For instance, data of some indicators was lacking, e.g., volume of treated wastewater, influent, or effluent concentrations of COD. When a WWTP does not have sufficient indicators, the WWTP was removed, and its emissions were not calculated. For the misreporting or misclassification, accurate classification of treatment technologies is the basis for calculating GHG emissions of secondary biological treatment processes, but uncertainties caused by misreporting and/or misclassification of treatment technologies are possible and cannot be easily rectified.

Data Records
The dataset of "Greenhouse gas emissions of wastewater treatment plants in China   We also assumed that there was no CO 2 generation under the pathway of 'flowing sewer' . Discharge pathway 6 and 7 were regarded as discharge into soil in this study. From IPCC 2019, default CH 4 emission factor of the pathway of discharge into soil was 0 g CH 4 /kg COD effluent. We did not consider CO 2 emissions of discharge into soil, because of a lack of data on the CO 2 emission factor of discharge into soil. www.nature.com/scientificdata www.nature.com/scientificdata/ In this study, the firm-level GHG emission inventory provides a foundation for the remaining emission inventories. Based on the firm-level GHG emission inventory, annual CH 4 , N 2 O and CO 2 emission inventories of biological treatment processes, effluent and electricity consumption are presented, and annual total CO 2 eq emissions of different technologies from biological treatment processes, electricity consumption and discharge pathways are also quantified.  Table 1. Since the enhanced activated sludge process is the main wastewater treatment technology in China and it includes many sub-categories, the emission structure of sub-categories (i.e., AO, A 2 O, OD and SBR) of the enhanced activated sludge process is also shown in pie charts.  Table 6. For comparison, we also list the uncertainty of CH 4 (Table 6). However, on-site emission factors of certain processes are rarely reported in the literature, and we cannot obtain their emission factors based on detailed process classification. For example, we applied a CH 4 emission factor (200 g CH 4 /kg COD) of the anaerobic process from IPCC 2019 to four different anaerobic processes (i.e., anaerobic hydrolysis, typical anaerobic reactors, anaerobic biofilter, and other anaerobic biological treatment), due to a lack of their on-site emission factors from references. Therefore, reported uncertainties (−30%,39%) for CH 4 emission factors of the four anaerobic processes are the same. Overall, the uncertainties of GHG emission factors of different biological treatment technologies were www.nature.com/scientificdata www.nature.com/scientificdata/ relatively high. One of the main reasons is that GHG emission factors are strongly affected by different operational parameters [46][47][48][49] (temperature, pH, dissolved oxygen (DO), sludge retention time (SRT), hydraulic retention time (HRT), influent chemical oxygen demand (COD) to total nitrogen ratio (C/N), influent chemical oxygen demand (COD) to total phosphorus ratio (C/P), etc.) of these WWTPs.
The uncertainty of CH 4 , N 2 O and CO 2 emission factors of 10 discharge pathways is shown in Table 7. Since CH 4 and N 2 O emission factors for the discharge pathway of 'flowing sewer (open or closed)' are zero in IPCC 2019, we assumed that there was no CO 2 generation under this flowing condition. We regarded discharge pathways via municipal WWPTs, centralized industrial WWTPs and other facilities (decentralized wastewater treatment facilities) as discharge pathway of 'flowing sewer' . Therefore, we do not report any uncertainty of CH 4 , N 2 O and CO 2 emission factors of entering municipal WWTPs, industrial WWTPs and other facilities. We considered the discharge pathway of 'other discharge pathways' in this study as 'discharge to aquatic environments (Tier 1)' in IPCC 2019, and its uncertainties of CH 4 (−100%, 148%) and N 2 O emission factors (−90%, 1394%) are the largest compared with other discharge pathways. Because there are very few studies on the CO 2 emission factor of the treated effluent, we derived CO 2 emission factors of lakes, rivers and reservoirs from the appendix of IPCC 2019 (IPCC 2019, Volume 5, Chapter 6, Page 59-Page 60) 15 , and we assumed that pathways of discharging into sea and 'others' also have the same CO 2 emission factors. Thus, their CO 2 emission factor uncertainties were all the same, with the uncertainty of (−12%, 20%). www.nature.com/scientificdata www.nature.com/scientificdata/ Combined uncertainty of GHG emissions. The combined uncertainty of GHG emissions of biological treatment processes is presented in Table 8 and Fig. 5(a-c). The shadow areas shown in Fig. 5 indicate the 95% confidence interval of GHG emissions. For comparison, CH 4 and N 2 O emissions calculated by emission factors of IPCC 2019 are also shown in Fig. 5(a,b). From 2006 to 2019, the uncertainties of CH 4 , N 2 O and CO 2 emissions in this study were (−57%, 124%), (−63%, 184%) and (−43%, 38%), respectively. But uncertainties of CH 4    www.nature.com/scientificdata www.nature.com/scientificdata/ minimum and maximum CH 4 and N 2 O emissions calculated by IPCC 2019 were all outside of the shadow areas in Fig. 5(a,b), reflecting larger uncertainties than in our study.
The combined uncertainty of effluent GHG emissions is presented in Table 9 and Fig. 5(e-g). The overall uncertainties of the effluent N 2 O were very high (−33%, 1161%), mainly resulting from high uncertainty of the effluent N 2 O emission factor (−100%, 1394%). N 2 O emission factors vary substantially between WWTPs, due to different process designs and operational conditions 46,47 . Effluent CH 4 and CO 2 emission uncertainties were relatively low, with values of (−52%, 29%) and (−9%, 16%), respectively. The uncertainty of total GHG emissions of WWTPs are shown in Fig. 5(h) and Table S4. The uncertainties of total GHG emissions from WWTPs were about (−27%, 97%).
Comparison with existing estimations. Several studies on CH 4 or N 2 O emissions of WWTPs at the national level in China have been reported [7][8][9][10][11][12][13] . In Table S5, we list wastewater GHG estimations in the literature for comparison. In most cases, the current estimation results are not comparable. The use of different system boundaries across studies is one of the main reasons. For instance, CH 4 emissions (76.2 Mt CO 2 eq) of wastewater from China's second biennial update report on climate change 50 in 2014 refer to emissions from both industrial and domestic wastewater at the national level and activity data was obtained from the Environmental Statistics Yearbook, while Zhao et al. 10 4 emissions. Our uncertainty analysis shows that CH 4 emissions calculated by IPCC 2019 are about 20%-62% larger than our research, and uncertainties caused by IPCC 2019 were much higher than in this study. In other cases, emission factors from the literature without distinguishing different technologies were used to estimate GHG emissions. For example, the MCF of 0.165 was used to calculate CH 4 emissions induced by domestic wastewater in several studies [7][8][9]12 . By using MCF 0.165, CH 4 emissions from domestic wastewater were around 28 Mt CO 2 eq in 2014 in Du et al. 7 While Yan et al. 11 obtained that the estimated CH 4 emissions were 0.77 Mt CO 2 eq in 2014 by using the emission factor of 2.3064 kg CH 4 /t COD removed. The discrepancy of CH 4 estimations between Du et al. 7 and Yan et al. 11 in 2014 was nearly 36 times. In comparison, estimated CH 4 emissions in our study are 2.6 Mt CO 2 eq. Comparing Guo et al. 13   www.nature.com/scientificdata www.nature.com/scientificdata/ COD (or BOD) mass minus COD (or BOD) removed in the form of sludge means that organic components transferred to sludge do not generate direct CH 4 , but only the remaining organic matter in the wastewater has potential to emit CH 4 . Therefore, the unit (kg CH 4 /kg BOD or kg CH 4 /kg COD) of CH 4 emission factor in IPCC indicates CH 4 emissions per unit remaining organic mass in the influent after considering COD (or BOD) www.nature.com/scientificdata www.nature.com/scientificdata/ transferred to the sludge, rather than CH 4 emissions per unit influent COD (or influent BOD) or per unit COD (or BOD) removed 9 . In addition, organic matter removed in the form of sludge was assumed as being zero for all treatment technologies 7-10,12 . The reasons for the incorrect assumption may be the lack of data on sludge generation, and the method to estimate organic components removed in the form of sludge is not mentioned in IPCC 2006, or the lack of background on wastewater treatment. The assumption may overestimate CH 4 emissions as most aerobic biological treatment technologies generate sludge during wastewater treatment. However, IPCC 2019 updated the method to account CH 4 emissions based on IPCC 2006, especially providing equations and detailed information to estimate COD (or BOD) transferred to sludge, which provides guidance for accurate CH 4 accounting.

Limitations.
We have four main limitations in this study. (1) A WWTP may have one or more wastewater treatment streams, and for each treatment stream, it may contain primary, secondary or tertiary treatment processes, while for each process (normally for a secondary treatment process), it has multiple treatment technologies. But to simplify GHG emissions estimation of biological treatment technologies of the secondary treatment process of a WWTP, the decision tree (Fig. 2) was applied to determine the main category of treatment technology and its corresponding emission factors, especially when a WWTP has several secondary treatment technologies. (2) Our emission factors of different biological treatment technologies were not based on the monitoring of each wastewater treatment plant. But we used emission factors in line with Chinese conditions. The emission factors were acquired from different references, such as on-site monitoring of specific biological technologies or modelling estimations in the literature, which was based on case studies of WWTPs in China. However, emission factors of some biological technologies, such as CH 4 and CO 2 emission factors of anaerobic technologies and constructed wetlands, were missing for China, thus we used IPCC emission factors for these technologies instead. On the other hand, given that emission factors of a specific biological treatment technology are greatly affected by operational conditions, different WWTPs with the same biological technology may have different emission factors. Therefore, GHG emission factors of a biological technology obtained from references are not representative for real emission factors of all WWTPs with the same technology. (3) GHG emissions from industrial WWTPs are not available and thus not included in our study although being important GHG emission sources of wastewater treatment systems [52][53][54] . For instance, Xing et al. reported that CH 4 emissions from on-site industrial wastewater treatment were always higher than that of domestic wastewater treatment between 2003 and 2008 in China. CH 4 emissions from industrial and domestic wastewater treatment were 0.95 Mt and 0.91 Mt respectively in 2008 54 . (4) Anthropogenic CO 2 emissions (or fossil CO 2 emissions) from biological treatment processes and discharge pathways are of main concern compared with biogenic CO 2 emissions, but we did not calculate fossil CO 2 emissions separately, because the CO 2 emission factors available in the literature are only reported as total CO 2 , rather than separate fossil and biogenic CO 2 .

Code availability
The scripts used to calculate firm level GHG emissions of wastewater treatment facilities are available in the Zenodo repository: https://doi.org/10.5281/zenodo.6052815 55 .  Table 9. The combined uncertainty of GHG emissions from effluent.